The timely identification of cardiac arrhythmia can be an important tool in the diagnosis, monitoring and treatment of abnormal heart activity. Current waveform morphologies and time domain parameter analysis of the depolarization and repolarization of the heart, such as P wave, QRS complex, ST segment, and T wave, are used for cardiac arrhythmia monitoring and identification. However, the waveform morphologies and time domain parameter analysis involves with these techniques are sometimes subjective and time-consuming, and require expertise and clinical experience for accurate and proper cardiac rhythm management. Recent efforts have aimed to apply more sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis, symbolic complexity analysis and nonlinear entropy elevation. Most of these efforts have focused on generating a new pathology index for qualitative cardiac arrhythmia characterization. There are several shortcomings with these clinical investigations and biomedical research strategies.
For example, morphology and time domain index evaluation of the electrophysiological signals are subjective, and can result in inaccurate interpretation and delayed cardiac rhythm management and treatment. Furthermore, there are no criteria of signal morphology evaluation or parameter analysis for intra-cardiac signals and arrhythmia characterization. For example, the threshold of the ST segment changes during intra-cardiac myocardial ischemia/infarction are not 0.1 millivolt (mV) as that in surface ECG signals. Thus, current criteria for ischemia identification and detection is ineffective for intra-cardiac electrograms, and thus new methods are needed for cardiac arrhythmia analysis and detection.
Also, recent research has focused on techniques such as frequency and symbolic analysis to calculate the irregular index of the cardiac signals. It is difficult, however, to map an irregular index onto the severity of the cardiac pathologies, and thus, cardiac arrhythmia analysis and related irregularity calculations have not been successfully used to diagnose and interpret the level and severity of the cardiac pathologies. Furthermore, these research methods have not combined the waveform morphology information, time domain and frequency domain analysis and calculation.
In summary, current clinical methods and research approaches cannot efficiently and automatically differentiate arrhythmias, categorize/map cardiac pathological severities and predict life-threatening disorders. Current clinical methodologies for cardiac arrhythmia calculation and evaluation also may generate inaccurate and unreliable data and results because of unwanted noise and artifacts. Environmental noise and patient movement artifacts, including electrical interference, can distort the waveform and make it difficult to detect R wave and ST segment elevation accurately.
Further, current cardiac applications and methods also cannot efficiently analyze and achieve real time growing trend and prediction of cardiac arrhythmias, such as the pathology trend from low risk to medium, and from medium risk to high risk (severe and fatal) rhythm (especially in one arrhythmia, such as ventricular tachycardia (VT) growing from low risk to high risk).